As the population increases across parts of the world, many cities are forced to deal with high traffic volumes, which results in traffic congestion. In an attempt to ease the congestion, many cities are working on implementing shared passenger transportation services, including rapid transit systems, trains, monorails, trams, light rails, and other types of commuter rail systems. Use of shared transportation can reduce the number of vehicles on the roads, which in turn lessens the traffic congestion.
Although public transportation provides a popular option for reducing traffic, the vehicles can be expensive to maintain, including labor and parts replacement. For example, most public transportation vehicles, such as trains and busses, include doors that open and shut to allow passengers to enter or exit the vehicle. The doors must be regularly maintained to prevent unexpected malfunctioning, which may result in unscheduled downtime of the vehicle for repair, disruption of passenger pick-up schedules for the vehicle, and customer dissatisfaction.
Generally, maintenance is scheduled based on manufacturer guidance or lab testing. Yet, utilizing only the guidelines and lab testing for scheduling maintenance can fail to provide accurate results based on an actual condition of the vehicle, which results in unnecessary maintenance, such as changing parts that are still working. Currently, studies have been performed to assess a condition of automatic train doors using Vibrational Analysis for Remote Condition Monitoring. Specifically, vibrations of a door are measured as the door is moving to an open or closed position and the measurements are used to determine which components are likely to develop faults. However, other measurements, such as the door motor current, can be used to more accurately and specifically identify different conditions of the door, some of which are separate from the components, such as a lack of grease, an object stuck in the door, or an excess of dirt.
Therefore, there is a need for an approach to accurately identify current and future component conditions to improve the effectiveness of scheduled maintenance and to identify and prevent unexpected component failure.